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Introduction to Digital Signal Processing
Digital Signal Processing DSP is an increasingly important and powerful technology (Steven W. Smith, 2005) of electric and computer engineering. It can be implemented to improve the working capabilities of telecommunication devices like cell phones, wireless modems, and satellite communication systems (David J. Mehrl, Marion O. Hagler, 2004). Technically speaking digital signal processing can be defined as “the processing of analog signals in the digital domain” (D. Koenig, 1994). It is an emerging technology that is modifying and reshaping several concepts of a telecommunication system. DSP has dramatically affected several fields of electronics and engineering (Steven W. Smith, 2005).
Signals are an integral component of the science and engineering world. There are uncountable applications that generate signals like “remote space probes, voltages generated by the heart and brain, radar and sonar echoes and seismic vibrations etc” (Steven W. Smith, 2005). DSP is a technology that enables computers to understand these signals. It can be used in any application that involves the high-speed processing of sizeable numeric data (D. Koenig, 1994). The knowledge and information about DSP include the general concepts that are applicable to every field and specialized techniques that could be implemented in some specific area or field (Steven W. Smith, 2005). Our discussion about Digital Signal Processing will be focused upon the use of DSP for the implementation of digital filters and common uses and applications of DSP filters.
Use of DSP in Digital Filter Implementations
A very important use of Digital Signal Processing is in the implication of the digital filters. Experts believe that the broad and significant uses of DSP for digital filters have made the technology of DSP so popular and prevalent in the twentieth century (David J. Mehrl, Marion O. Hagler, 2004). There are two general purposes for which the digitals filters are generally used. The first use of digital filters is “signal separation” which is needed when it becomes difficult to receive a signal due to some interruption caused by any noise or due to the presence of any other signal (Steven W. Smith, 2005). In this situation, the digital filter is used to separate the signals so that they can be analysed individually.
The second use of the digital filters is the “signal restoration” which is needed when there is distortion of a signal due to any reason like poor equipment quality or mishandling of devices etc. in this situation the digital filters work to restore the signal and to make the signal quality better (Steven W. Smith, 2005). In both of the above situation another option is also present and that is the use of Analog or electronic filters which are cheaper and faster then the digital filters however the digital filters have proved to be superior then the analog filters and even thousand times better results could be obtain by using the digital filters.
The digital filters are used to calculate a time domain representation of sampled input (DSP Design Guide, 2003). DSP has a role to play here. The digitalized inputs are mathematically influenced by the DSP program. The DSP is used to change or transform the input signal through a particular process. The input signals are passed through a structure where the clocked data of the signals is converted in to “summers, delay blocks and multipliers” (DSP Design Guide, 2003). Through this arrangement the mathematical value of the signals is changed and finally the output is obtained which is the filtered signal or transformed signal. In this way the DSP is used for implementing the digital filters. It is a very complex procedure that involves large numbers of multiplications and additions. As a result of this calculation it is very common to have some errors or noise. To cope up with this problem changes could be made in the bit resolution (DSP Design Guide, 2003).
Another use of DSP in the implication of the digital filter could be in form of a programmable digital filter. The DSP could be used for the signal conditioning through software. If there would be a need to change the shape of the filter or the frequency response the DSP program could be used for this purpose. The stored or calculated coefficients could be loaded to the software and there will be change in the shape or response frequency of the digital filter (DSP Design Guide, 2003). The digital filters could be implemented through different ways. The impulsive response of the digital filter is kept with the input signal to make all the possible linear filters. The filter made through this process is known as the “filter kernel” which employs the impulsive response to get the filter (Steven W. Smith, 2005). This process is known as “finite impulse response or FIR”.
Another way of making the digital filter is called “recursion”. This is an extension of the process of making the kernel filters. A set of recursion coefficient is used to define the filters. This process is also known as “infinite impulse response or 11R” (D. Koenig, 1994). There is a difference in the performance level of both the filters. The filters made from the convolution could be slowly executed however their performance is much betters then the filters that are made by using the recursion method (D. Koenig, 1994). The division and classification of the filters with respect to the response they get is shown in the following chart.
Common Uses and Practical Applications of DSP Filters
The DSP plays significant role in the implication of the digital filters. This technology can be utilized in several important fields. It could be used for digital or integrated analog/digital product solutions (David J. Mehrl, Marion O. Hagler, 2004). The DSP chip helps in processing large data and makes it perfect to be used in “numerically intensive DSP algorithms” (D. Koenig, 1994). The DSP chip also has a capability of executing more then one operation at one time. There is wide range of common and practical implications of DSP filters some of them are described below.
The DSP filters allow real time control in applications. The DSP allows executing certain functions within specified intervals of time while there is no effect of the external interruptions. For example in a computer, it divides the real time task between the processor and the host CPU in a manner that they give their maximum performance and the key board inputs and disk accesses could be served by the main CPU and there will not be any effect on the program that is being run by the DSP board (D. Koenig, 1994).
Another noticeable use of the DSP filters is in the graphics and Image processing. As the graphic images are two dimensional arrays of numbers and the DSP hardware is also capable of operating the arrays so it become quite easy for the DSP filters to process even large amount of data that is to be used in the process of graphic or image processing. The DSP filters could also be used for the processing of Acoustics and Speech (D. Koenig, 1994). The importance of the DSP technology for the processing of the speech and acoustics is recognized many years ago and with the passage of time the significance of this technology is rapidly increasing in this field. There have been several advancements in the technologies like speech recognition, radar, audio research etc in the last few years. All these advancements and innovations become possible with the support of real time processing capabilities of DSP.
The DSP technologies also enhance the working capabilities of a computer. With the help of a plug in DSP board it become possible to modify the basic architecture of a PC. It enables a single CPU system to convert in to a dual-processor workstation. In the mean time the real time tasks run on the DSP board. The DSL technology provides effective communication protocol between the processors and as a result it become possible for two independent CPUs to run independently while the PC and board perform real time tasks (D. Koenig, 1994). The DSP also make possible the application of “real-time virtual instrumentation VI” (D. Koenig, 1994). There are several advantages associated with the virtual instrumentation and DSP technology made it possible to get benefits from all those advantages. For example the VI reduces the costs of several equipments and offers flexibility because if VI is implemented then there is no need of purchasing many instruments that perform different functions in the experiments (D. Koenig, 1994).
Conclusion
The above examples are some of the common and very important and practical uses of DSP filters. The significance of the DSP technology is accepted by the experts and the uses of DSP are also increasing in various sectors.
References
Steven W. Smith, “The Scientist and Engineer’s Guide to Digital Signal Processing”, Chap. 14, California Technical Publishing, 2006. Web.
David J. Mehrl and Marion O. Hagler, “Digital Signal Processing”, 2004. Web.
Applied Signal Processing at Efluids. Web.
DSP Design Guide, Frequency Devices, Inc., 2003. Web.
D. Koenig, Digital Signal Processing Fundamentals, 1994. Web.
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